A primary use of a population-genetic perspective in the GWA context has been in predicting expected patterns of disease variation113,114,115. However, GWA statistical analysis tools have not yet fully taken advantage of this perspective. From a population-genetic standpoint, all individuals have some degree of relationship through their shared descent in the complete human pedigree. In standard GWA analyses, however, in which alleles that are more common in cases than in controls are identified by testing contingency tables locally along the genome, an implicit assumption is that the genotypes of separate individuals can be treated as independent random variates. Approximating separate individuals as independent has been productive as a first approximation, but more information is potentially available by accounting for correlation among individuals owing to shared descent. Fine-mapping association methods designed for localization of risk variants do seek to consider this shared descent116,117,118,119,120. These methods have been informative on a small scale, and a current challenge is to extend them for large datasets.
